Neonatal seizures may be the most frequent, and often the only clinical sign of central nervous system dysfunction in newborn children. Such events can be difficult to identify clinically. Seizures raise immediate concerns about the underlying cause of brain disorder, associated clinical conditions, the effect seizures may have on the developing brain, the need for antiepileptic drugs, and the effect antiepileptic drugs may have on the newborn child. In preterm infants, the immature brain is resistant to acute-induced cell loss, but there are functional abnormalities following seizure with impairment in visual-spatial memory and reduced threshold. Neonatal seizures are also associated with many activity-dependent changes in brain development including altered synaptogenesis and reduction in neurogenesis. However, the inherent difficulty in recognizing seizures may limit the effectiveness of the clinician in the care of infant children, particularly those suffering seizures. In addition, because of the developmental immaturity of the brain, neonatal seizures may have unique clinical manifestations when compared to seizures in older infants, children, and adults. Seizures may be brief and infrequent and may also occur when trained personnel are not observing infants. Thus, the clinician may be left to rely on historical information that may be incomplete or inaccurate. As a result, neonatal seizures may be more difficult to recognize than those of older age groups. Further, seizures in this age group may not occur until several days after an insult, and some newborns are pharmacologically paralyzed to improve ventilation, thus making visual identification of seizure behavior impossible.
The sequelae of seizures during the newborn period can be significant, including for example death, abnormal neurologic findings, mental retardation, brain growth retardation, and development of postnatal epilepsy. Methods of multivariant analysis have been proposed to predict the ultimate outcome of newborns that have experienced seizures during the newborn period. Such methods include consideration of interictal electroencephalogram (EEG) features from one recording or from serial recordings, the ictal EEG, neurological examination at the time of seizure, character or duration of seizure, etiology, findings on neuroimaging, conceptional age (i.e. a child's age as measured from the date of conception), and birth weight.
In clinical practice, predicting outcome in the acute period is most reliably based on assessment of an initial degree of brain injury and persistence of brain dysfunction during the ensuing first few days. The EEG has an important role in the diagnosis and management of neurological disorders in newborns, and in at least one study was proven superior to clinical examination of newborns for early detection and prognosis of brain dysfunction. The EEG can also provide an important measure of quality of brain function in critically ill infants. Continuous video-EEG monitoring of critically ill children can provide information about the presence of seizures, and can be useful to monitor a child's response to anti-convulsant treatment. Further, EEG monitoring can be used to provide “real time” and continuous information concerning brain function, in contrast to other techniques such as head ultrasonography and neuroimaging studies which assess brain structure. EEG may identify pathological changes in brain function and provide prognostic markers, which can be useful to monitor response to therapy and aid clinical management, for example to determine duration of therapy. Sensitive and specific EEG measures can provide relevant information about brain function prior to clinical manifestation, which may present a window of opportunity for appropriate interventions. Monitoring can be used to determine whether the newborn is experiencing state changes in EEG, capturing a clinical seizure on video that is closely correlated with EEG seizure activity is a definite criterion for the monitoring-based diagnosis of a seizure of epileptic origin. However, identifying EEG seizures can be a challenging experience, even for the more experienced clinical neurophysiologist. Also, video-EEGs are often reviewed post-hoc, often several hours after a seizure actually occurred. In many neonatal intensive care units (ICUs), “real-time” interpretation of the EEG is not possible or practical, and few neonatal health care professionals have received sufficient training in neonatal EEG interpretation to assist with “real-time” EEG analysis at the bedside.
An ability to monitor brain function and dysfunction, and detect neonatal seizures in “real-time”, would greatly empower the clinician's ability to make timely and accurate medical decisions about the status of a newborn child's brain. An ability to identify a newborn at risk for brain problems in the acute setting and in real time through quantitative means, would provide a greater degree of control over this disorder so that clinicians can make more efficient and effective treatment and management decisions. Furthermore, quantitative identification of brain vulnerable states also offers the scientist better tools in formulating an understanding of the disorder. Validated quantitative measures of EEG activity may help to determine the degree of brain dysfunction, which in turn may provide some early clinical insight into long-term outcome.
The major new thrust of research work in EEG analysis is to extract information from EEGs, which is not available by visual analysis of the raw recording. More recently, the ongoing technical revolution in computer storage and processing power has allowed greater ability to collect and analyze comprehensive data sets. By quantitatively examining EEG data in adult patients with epilepsy, measurable sub-threshold changes that precede and accompany seizures have been reported. In particular, it has been suggested that brain electrical activity evolves to a measurable linear or nonlinear state of maximum order just prior to, and during seizure onset. Automatic seizure detection methods have been applied to newborns by using three types of analysis, including spectral analysis, spike detection, and low-pass digital filtering. Initial evaluation indicated that more than 70 percent of seizures, and almost 80 percent of seizure clusters, were detected with a false detection rate of 1.7 per hour. In the testing of data set, the method was validated using a completely new set of data from 54 patients, and the average detection rate was nearly 70 percent with a false detections rate of 2.3 per hour (Gotman et al., EEG and Clinical Neurophysiology, 103(3): 356-369, 1997).
These results suggest that there is potential for reliable automated seizure detection methods after the suspected insult.